It is not easy simulating a battery. Off-the-shelf simulation tools are not as much help as one might think. One can pick some real-life parameters that one thinks may be helpful in the simulation, and the off-the-shelf simulation tool may not be able to simulate all of the parameters.
Successful simulation of a battery can permit predicting, in advance, the service life of a proposed battery in a proposed application. Thus for example there may be empirical measurements as for a particular cell that may serve as a building block for a battery that has not yet been built. It may be desired to predict the service life for the not-yet-built battery in a particular application. Or it may be desired to predict the number of charge/discharge cycles that are likely to be available from the not-yet-built battery.
In addition to simulation of a not-yet-built battery, it can be very helpful to arrive at an estimate of state of charge or state of health for an actual battery in actual service. A successful (that is, accurate) estimate of state of charge would, in an electric car, permit a successful estimate of the traveling distance available to the driver before the battery runs out. In contrast an unsuccessful estimate can lead to a very disappointed user if the battery runs out sooner than expected, thereby stranding the user. Or an unsuccessful estimate can lead to a failure to take advantage of the full capacity of the battery, for example unnecessarily forgoing a particular diversion when the diversion would, in fact, have been possible to the user.
Likewise a successful estimate of the state of health of the battery permits planning. For example if the system correctly estimates that the state of health is poor, the user can arrange for a battery replacement and thus can avoid getting stranded somewhere due to battery failure. If on the other hand the system arrives at an inaccurate estimate, the user could schedule a wholly unneeded battery replacement session, wasting time and losing use of the vehicle during the trip to and from the service location. Alternatively the user could end up stranded somewhere due to a failure to estimate the (poor) state of health of the battery.
It will come as no surprise that many investigators have expended enormous amounts of time and energy attempting to develop simulation tools which might help with these real-life tasks. It will also come as no surprise that to date, no approach known to the applicant has worked out well. A successful approach would likely be “compact” as the term is used in the world of simulation, meaning among other things that it can be done with only modest computational expense while providing reasonably accurate simulation results.
International patent publication WO 2012/173937 A2 (published Dec. 20, 2012) and US patent publication US 20130282353 A1 (published Oct. 24, 2013), filed by the present applicant and inventor, discuss earlier approaches to the difficult problem of simulating battery conditions. As described there, a battery simulator is made up in part of nodes, each individually simulated, and the nodes communicate with each other by means of values which within the domain of the simulator are understood as voltages but which may have real-world significance for some value that is not a voltage at all.